The results of the multicollinearity test are presented in a tabbed sheet.
A multiple regression is undertaken for each of the environmental variables in turn
with all the other environmental variables acting as the independent variables. Values
of R squared close to 1 or Variance inflation factors (VIF) well above 1 are indicative
of multicollinearity and you should consider removing one of a group of highly
correlated variables from the analysis. High VIF values are highlighted in red. Use
to identify which environmental variables
to retain for your analysis
If you should obtain very large values for VIF it is likely that some of your variables
add up to a constant. This often happens if you are using dummy variables see
Linear Combinations of Environmental Variables
Circular environmental variables
Two common examples of circular data are direction (aspect) and day of the year.
Aspect (or compass direction of a slope) can be transformed by trigonometric
functions (Roberts 1986). The simplest way to do this is to create two variables,
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